Namangan Center Neighborhood Scan

scrollama = require("scrollama@3.1.1");
scroller = scrollama();
d3 = require("d3");

// Define variables (taken from template, might not need all of them)
$container = d3.select("main.content");
$graphic = $container.select(".maps");
// $chart = $graphic.select("#scrollama-test")
$text = $container.select(".text-column");
$step = $text.selectAll("section.level3");
$layerControl = $graphic.select(".leaflet-control-layers-list");
$overlaysControl = $layerControl.select(".leaflet-control-layers-overlays");

overlayLayers = Array.prototype.map
  .call($overlaysControl.node().getElementsByTagName("span"), span => {
    return(span.textContent.replace(/\s/g, ""))
  }) // Need to replace white space with nothing because .textContent returns a leading space

// console.log(overlayLayers)

// Hides all legends. I'm not sure why the legends continue to show
// at the start even when I use `hideGroup`, so this is my workaround
// (Should use overlayLayers but I don't feel like testng right now)
arry = Array.from(document.getElementsByClassName("leaflet-control-layers-overlays")[0]
  .getElementsByTagName("input"));
arry.forEach(element => element.click())

function checkLayerStatus(spanText) {
  var inputs = $overlaysControl.node().getElementsByTagName("input")
  var index = overlayLayers.indexOf(spanText)
  return(inputs[index].checked)
}
function handleStepEnter(response) {
  // response = { element, direction, index }

  // console.log("Enter triggered")
  // console.log(response)

  // console.log(layers)
  // console.log($overlaysControl.html())
  // console.log(response.element.id)

  $step.classed("is-active", function (d, i) {
    return i === response.index;
  })
  
  $graphic.classed("is-active", function (d, i) {
    var colorRows = [1, 3]
    return colorRows.includes(response.index)
  })
  
    // This is for testing purposes and requires a level 3 header and a paragraph above the map
  // var header = $graphic.select("h3")
  // var mapText = $graphic.select("p")
  // header.text(response.element.id)
  
  var layerIndex = overlayLayers.indexOf(response.element.id)
  if (layerIndex >= 0) {
    // This is for testing purposes and requires a level 3 header and a paragraph above the map
    // mapText.text(response.element.id);
    // console.log(response.element.id + ": " + checkLayerStatus(response.element.id));
    if (!checkLayerStatus(response.element.id)) {
      $overlaysControl.node().getElementsByTagName("input")[layerIndex].click()
    }
  }
}

function handleStepExit(response) {
  // console.log("Exit triggered")
  // console.log(response)
  // { element, index, direction }
  response.element.classList.remove("is-active");
  
  var layerIndex = overlayLayers.indexOf(response.element.id)
  if (layerIndex >= 0) {
    //console.log(response.element.id + ": " + checkLayerStatus(response.element.id));
    if (checkLayerStatus(response.element.id)) {
      $overlaysControl.node().getElementsByTagName("input")[layerIndex].click()
    }
  }
}
// initialise the library and pass event handlers to 
function init() {
  scroller
    .setup({
            container: 'main.content', // our outermost scrollytelling element
            graphic: '.maps', // the graphic
            text: '.text-column', // the step container
            step: '.text-column section.level3', // the step elements
            offset: 0.5, // set the trigger to be 1/2 way down screen
            debug: false, // display the trigger offset for testing
    })
    .onStepEnter(handleStepEnter)
    .onStepExit(handleStepExit)
}

init();

$section2 = $text.selectAll("section.level2");
$headerAboveMap = $container
  .select(".navigation")
  .select("ul")
  .select("li")
  .select("#dynamic-header")

console.log($headerAboveMap)

scrollerSectionTitle = scrollama();
function init2() {
  scrollerSectionTitle
    .setup({
            // container: 'main.content', // our outermost scrollytelling element
            // graphic: '.maps', // the graphic
            // text: '.text-column', // the step container
            step: '.text-column section.level2', // the step elements
            offset: 0.5, // set the trigger to be 1/2 way down screen
            debug: false, // display the trigger offset for testing
    })
    .onStepEnter(response => {
      console.log("ENTERED")
      var header = $headerAboveMap
      // var mapText = $graphic.select("p")
      console.log(response.element.firstElementChild.textContent)
      console.log(header.text())
      //.getElementsByTagName("h2"))
      header.text(response.element.firstElementChild.textContent)
      // navigation.select("ul").select("li")
    })
}

init2();

Namangan Center Neighborhood Scan

• Neighborhood Scan ∨
  • Interactive: Inititate the map here, will build on it in each “slide”
  • Static:
  • Executive Summary
  • Setting the Context
    • Basic City Information
    • The State of Urban Infrastructure and Service Delivery
  • Urban Cover Dynamics
    • Land Cover
  • Climate Conditions
    • Summer Surface Temperature
    • Land Cover and Temperature
    • Urban Thermal Field Variance Index
    • Vegetated Areas
    • Elevation
    • Slope
    • Moisture print_slide_text(slide_texts\(risk_identification\)fwi)NA

[Inputs version]

Namangan Center Neighborhood Scan

Executive Summary

Setting the Context

Namangan is the second largest city in Uzbekistan. Over the past two decades it has grown at almost 3% annually, a fairly young population. Most built-up areas are concentrated greatest in the city center.

Urban Cover Dynamics

Namangan’s center is mostly built-up, which are more compact in the South Eastern and less compact in the North Western. Also there are large areas of bare/sparse vegetation along the North Eastern to South Western water feature. Tree cover are mostly concentrated in the center, and very sparse grasslands.

Climate Conditions

Surface temperatures are higher bare/sparse vegetation along the North Eastern to South Western water feature. The temperatures and are relatively color in the South Eastern part. Most of the cooler temperatures are on vegetated and water surfaces.

Local Institutions and Planning

Urban planning is centralized in Uzbekistan, with the Ministry of Construction responsible for land use planning, and three national entities responsible for sub-national master and development plans. Uzbekistan has a National Development Strategy for 2030. Namangan has a regional development strategy until 2030; its most recent master plan is form 2007.

Setting the Context

Basic City Information

Namangan is geographically situated at 41° 0′ 4″ N, 71° 40′ 6″ E, covers an area of 145 km², with a population of about 678 000 in 2023, making it the second-largest city in Uzbekistan. The city is bordered to the North by Tian Shan Mountain range and to the South by the Fergana Valley. Before 2005, the average annual increase in population was 2.5%, but has since risen to 2.9%.Namangan is a significant hub of light industrial activities, including craft, trade, and food processing.

The city has extreme temperature variability, with extreme cold winters (average of -2.3 °C) and hot summers (average of 26.3 °C). Also, the city elevations range from 620 to 820 meters with steep slopes, making it susceptible to landslides. With increasing urbanization, the exposure and vulnerability to disaster risk will likely intensify.

The black and yellow boundary marks the area of interest for the Neighborhood Scan. Weather and Climate, N.D.; UNECE, 2017. Climate classification from Kottek et al’s 2006 Köppen-Geiger update.

The black and yellow boundary marks the area of interest for the City Scan.

The State of Urban Infrastructure and Service Delivery

Land Administration

Since gaining independence from Soviet rule, Uzbekistan has shifted towards private ownership of urban or non-agricultural land, with most land now held under different types of leaseholds. Housing, particularly in urban areas, is predominantly privately owned. The land administration system in Uzbekistan is a subject of ongoing reform, with initiatives aimed at improving the effectiveness of state ownership of land, the consolidation of agricultural land, and the development of an agricultural land market.

Energy

Since 2021, the Namangan region has been undergoing a significant transition in its energy sector, including the development of a large-scale solar project known as the Namangan Site Solar PV Park, spanning over 700 hectares. These initiatives form part of Uzbekistan’s broader energy sector reforms, aiming to shift from a government-owned and operated energy sector model to competitive markets in gas, oil, and electricity. .

Housing

The housing sector in Uzbekistan faces several challenges, including affordability, supply, and quality issues. State ownership of urban land has acted as a disincentive for the construction of additional housing units, resulting in a low yearly construction rate of 1.9 units per 1,000 people. The government aims to increase the volume of housing construction by 1.5 times compared to the previous year.

Disaster Risk Management

Effective disaster risk management is critical in Uzbekistan, a region susceptible to various natural hazards, including flooding, earthquakes, drought, landslides and extreme heat. These risks are compounded by factors such as rapid urbanization, and aging infrastructure. Since 1997, the Government of Uzbekistan has made significant progress in disaster risk management, transitioning from a reactive approach focused on emergency responses to a proactive strategy aimed at mitigating disaster risks.

Drinking Water Supply

As of 2021, Namangan faced significant challenges with its drinking water supply and quality, with numerous households lacking connections to the centralized water supply system. The Improvement of Water Supply in Yangikurgan District and the City of Namangan Project, launched in 2020 and spearheaded by the Uzbekistan Ministry of Housing and Communal Services, aims to replace and rebuild water supply infrastructure in these areas, benefiting approximately 185,000 people in these communities.

Urban Roads & Transport

The Namangan Regional Development Strategy (2022-2030), intends to invest in key transport infrastructure, with a particular emphasis on road and rail projects. However, despite Namangan’s expressed interest in developing green urban transport corridors (GUTC) and expanding the fleet of electric buses, no financial commitments have been made toward these initiatives.

Solid Waste Management

The Sustainable Solid Waste Management Project, funded by the Asian Development Bank, is working to strengthen institutional capacity for solid waste management. The project also focuses on raising public awareness and fostering community participation in waste reduction and recycling across cities and regions in Uzbekistan, including Namangan.

Local Administration

Uzbekistan operates as a unitary presidential republic, with a subnational two-tier system involving elected local councils and appointed local state executive governments. However, Uzbekistan is still characterized by a relatively high degree of centralization in decision-making.

Land Administration: UNECE, 2017; UNECE, 2017. Housing: UNECE, 2017. Energy: International Energy Agency, 2023; UNECE, 2017. Disaster Risk Management: UNDP, N.D.; Prevention Web, 2020. Water: UNECE, 2017. Roads and Transport: European Union, Council of Europe, N.D.; UNECE, 2017. Solid Waste Management: Asian Development Bank, 2019; UNECE, 2017. Local Administration: Asian Development Bank, 2019.

Urban Cover Dynamics

Land Cover

• Built up class covers 58.6% of the total area in 2011, 57.5% in 2017, and 58% in 2023
• Most of the city is concrete, with patches of trees in the center, and dispersed patches of grassland across the entire city.
• There are two large patches of bare/ sparse vegetation in the north.

Land cover refers to the ground surface cover, including vegetation, built-up, water, and bare soil, among other classifications. Identifying land cover type helps to understand land utilization and inform planning policies and programs. In the sankey diagram the straight gray bars represent the amount of area that remains unchanged from 2011 to 2023.

© WorldView Images, 2011, 2017, and 2023. We acquired very high-resolution images from WoldView, and randomly generated and interpreted points in Google Earth Pro 7.3. The interpreted data have dates ranging from 2011, 2017, and 2023 and five urban cover classes, including built-up, bare soil, trees, grass/shrub, and water. The points were partitioned into 70% for training and 30% for validation of random forest classifier. Overall accuracy (OA) indicates the percent of the reference samples that are correctly mapped when compared to the total reference samples. OA was 87.15% in 2011, 86.75% in 2017, and 89.13% in 2023. Kappa measures the agreement between categorical variables in the predicted map and the reference map, with values ranging from -1 to 1. Negative values mean the classification is worse than randomly assigned values, 0 means, and random classification are similar, and values closer to 1 mean perfect classification. Thus, higher kappa coefficient values in land cover change classification are generally preferred to low values. The kappa coefficient in 2011 was 0.8178; in 2017, it was 0.8094; and in 2023, it was 0.8217.

Climate Conditions

Summer Surface Temperature

• The mean temperature was 39.7°C in 2011, 46.7°C in 2017, and 45.3°C in 2023
• The highest temperatures in 2011, 2017, and 2023 are consistently concentrated in the North

Temperatures in an area are affected by many factors, such as land cover, elevation, slope, and proximity to water. Higher temperatures can generate or exacerbate negative effects related to health, social equity, and economic productivity. Typically, cities demonstrate higher temperatures than vegetated areas: construction materials, such as concrete, absorb more solar radiation; less vegetation results in less evapotranspiration; and more vehicle usage and mechanical cooling generate more heat. This map shows average surface temperatures from June through September, 2017–2021, at a 30-meter resolution. Note that it measures surface temperature rather than ambient temperature, which can differ by several degrees. Surface temperature is primarily useful for identifying hotter and cooler areas within a specific geography.

Landsat Level 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey.

Picking joint bandwidth of 0.98
Picking joint bandwidth of 0.98

• The temperature varies across the city center with the lowest in permanent water bodies and highest in bare/sparse vegetation in 2011, 2017, and 2023.
• The temperature was consistently highest in 2017 for all urban land cover and land use type

We randomly and manually selected points across and extracted the land cover and summer temperature for 2011, 2017, and 2023. The figure displays the relationship between land cover types and temperature in Namangan city center.

Urban Thermal Field Variance Index

• Areas with relatively lower temperature than the mean in 2011 cover 63.2% of the study area, and this value decreased to 52.3% in 2023.
• Areas with strong to strongest increase in temperature than the mean cover was 30.6% of the study area, and this increased to 33.6% in 2017, and to 34.6% in 2023.

Urban thermal field variance index (UTFVI) is the ratio of the differences between land surfaces and the mean temperature. It is both qualitative and quantitative index used to describe the vulnerability and effect of surface urban heat islands, with values of less than zero meaning no UHI effect and values greater than 0.020 meaning the strongest UHI effect (Al Kafy et al., 2021; Singh et al. 2017). The map shows average urban thermal field variance from June through September each for 2011, 2017, and 2023, at a 30-meter resolution. The UTFVI is primarily useful for identifying and grouping the extent of temperature changes.

Landsat Level 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey.

Vegetated Areas

• Green spaces are very scarce, however, the city center have relatively more greenery than surrounding neighborhoods

This map displays the Normalized Difference Vegetation Index, which ranges between -1 and 1, with higher numbers indicating a higher density of green vegetation. Values of less than 0.1 typically indicate water, rock, and otherwise barren land; values of 0.1 to 0.5 are associated with sparse vegetation, such as shrubs and grassland; and values of more than 0.5 correspond to dense vegetation such as forests or mature crops. Vegetation and green spaces in cities are associated with health benefits and the mitigation of environmental risks. More green space in a city can reduce temperatures and the urban heat island effect, lessen air pollution, and absorb floodwaters. Green spaces can also serve important civic, social, and quality of life functions.

© WorldView Image for 2017, “Normalized Difference Vegetation Index”. NDVI-based measures do not account for the proximity and spatial arrangement of green spaces within areal units. They also do not address the vertical dimension and density of green urban buildup.

Elevation

• The elevation ranges from about 410 to 580 meters above sea level. The lower elevations are in the south eastern part of the city center, and the higher elevation in the north western
• About half (50%) of the city center have elevation from 410 to 442 meters

Elevation informs an area’s vulnerability to many natural disasters. The height at which infrastructure, resources, and communities sit relative to normal water levels and tides, flood waters, and storm surges and waves informs their exposure. Elevation information is critical for communities to anticipate the impacts of disasters and to prepare resilient and cost-effective response and redevelopment strategies.

Map data from USGS, 2015, “USGS EROS Archive - Digital Elevation”.

Slope

• Most of the built-up areas are concentrated slopes of grade between 0-10 degrees, representing 97.7% of the study area.
• The north eastern parts of the city are a lot steeper than, and few places have slopes over 10°.

Slope refers to the percentage change in elevation over a certain distance. In hilly or mountainous areas, floods can occur within minutes after heavy rains, while in flat areas, floodwaters can remain for days. Considering the slope of land is important in reducing construction costs, extending services and public facilities, minimizing the risks of hazards like flooding and landslides, and mitigating the impacts of development on natural resources.

Map data from USGS, 2015, “USGS EROS Archive - Digital Elevation”.

Moisture

• Due to built-up areas, most of the city center has low level of soil moisture, which indicates water stress
• The soil surfaces and the north western part is drier than the central and eastern side

The Normalized Difference Moisture Index (NDMI) detects moisture content in vegetation, and is an indicator of water stress in crops. It is also used to identify vegetation in dry areas with an increased risk of combustion. This map computes NDMI for June 2017. NDMI measures soil moisture from a range of -1 to 1. Negative values indicates water stress, and positive values may indicate water-logging. The stresses introduced by low soil moisture are borne by agricultural crops and biodiversity. Increased depletion of soil moisture also leads to a higher risk of wildfire.

© World View Images for 2017